计算技术与自动化2026，Vol.45Issue(1)：151-157,7.DOI:10.16339/j.cnki.jsjsyzdh.202601024
基于改进残差网络和SHAP的糖尿病预测及可解释性分析
Diabetes Prediction and Interpretability Analysis Based on Improved Residual Network and SHAP
摘要
Abstract
To address the lack of reliability and interpretability in the field of diabetes prediction,a prediction algorithm based on improved deep residual network is proposed.The algorithm embeds a feature self-attention mechanism designed ac-cording to the characteristics of the dataset,and is complemented by the SHAP model to enhance the interpretability,which can pinpoint and visualise the key factors affecting the prediction of diabetes mellitus,and enhance the transparency and practical value of the prediction logic.The experiments were carried out on the public dataset of Pima and the private dataset of a tertiary general hospital in Qingdao,and the RAC model was compared with the plain Bayes,logistic regression,and support vector machine models.The results show that the classification accuracy,sensitivity,specificity,and Fl score val-ues of RAC are better than those of other models,validating its potential for early warning or assisted diagnosis in clinical practice.关键词
糖尿病预测/可解释性/改进深度残差网络/特征自注意力机制/SHAP模型Key words
diabetes prediction/interpretability/improved deep residual network/feature self-attention mechanism/SHAP model分类
信息技术与安全科学引用本文复制引用
魏国政,魏丽丽,宋廷强,渠蓉蓉,孙媛媛,董凡琦..基于改进残差网络和SHAP的糖尿病预测及可解释性分析[J].计算技术与自动化,2026,45(1):151-157,7.基金项目
国家自然科学基金青年项目(32301702) （32301702）
中华护理学会科研项目(ZHKY202118) （ZHKY202118）
山东省护理学会科研项目(SDHLKT202209) （SDHLKT202209）
